Methods for more effectively moderating one or more images and devices thereof

ABSTRACT

Methods, non-transitory computer readable media, and image moderation management computing apparatuses that receive a request that includes an image to be moderated. A percentage match of the received image against one or more computer vision models for one or more different categories is identified. A determination is made when the percentage match is within a range between customizable lower and upper thresholds for one or more of the different categories. Image moderation analysis data on the received image from one of one or more moderator computing devices is obtained when the percentage match is within the range. One or more stored rules on the received image are executed based on the obtained image moderation analysis data.

FIELD

This invention relates to methods for more effectively moderating one ormore images and devices thereof.

BACKGROUND

In a variety of different types of environments, such as at home, in apublic setting, or at work by way of example, there is an ongoing needand desire to be able to review, sort and moderate images. Additionally,regardless of the type of environment, there also may be vastlydifferent requirements for reviewing, sorting, and moderating imagesbased on other characteristics, such as age and/or culture, by way ofexample. Unfortunately, current image moderations systems are notdynamic enough to quickly and effectively adjust to all of thesedifferent possible characteristics.

SUMMARY

A method for moderating one or more images implemented by an imagemoderation management system comprising one or more image moderationmanagement computing apparatuses or one or more moderator computingdevices, the method includes receiving, by an image moderationmanagement computing apparatus, a request that includes an image to bemoderated. A percentage match of the received image against one or morecomputer vision models for one or more different categories isidentified. A determination is made, by the image moderation managementcomputing apparatus, when the percentage match is within a range betweencustomizable lower and upper thresholds for one or more of the differentcategories. Image moderation analysis data on the received image fromone of one or more moderator computing devices is obtained, by the imagemoderation management computing apparatus, when the percentage match iswithin the range. One or more stored rules on the received image areexecuted, by the image moderation management computing apparatus, basedon the obtained image moderation analysis data.

An image moderation management computing apparatus, comprising memorycomprising programmed instructions stored thereon and one or moreprocessors configured to be capable of executing the stored programmedinstructions to receive a request that includes an image to bemoderated. A percentage match of the received image against one or morecomputer vision models for one or more different categories isidentified. A determination is made when the percentage match is withina range between customizable lower and upper thresholds for one or moreof the different categories. Image moderation analysis data on thereceived image from one of one or more moderator computing devices isobtained when the percentage match is within the range. One or morestored rules on the received image are executed based on the obtainedimage moderation analysis data.

A non-transitory computer readable medium having stored thereoninstructions for moderating one or more images comprising executablecode which when executed by one or more processors, causes the one ormore processors to receive a request that includes an image to bemoderated. A percentage match of the received image against one or morecomputer vision models for one or more different categories isidentified. A determination is made when the percentage match is withina range between customizable lower and upper thresholds for one or moreof the different categories. Image moderation analysis data on thereceived image from one of one or more moderator computing devices isobtained when the percentage match is within the range. One or morestored rules on the received image are executed based on the obtainedimage moderation analysis data.

This technology provides a number of advantages including providingmethods, non-transitory computer readable media, image moderationmanagement systems, and image moderation management computingapparatuses that provides more effective moderation of images, such asavatars, profile pictures, contest entries, and photo album pictures byway of example. Additionally, with this technology customizable feedbackfrom a moderator can be quickly and easily incorporated in. Further withthis technology, moderation of images can easily be customized based notonly on the particular setting or environment, but also based on one ormore characteristics, such as age and/or culture, by way of example.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a diagram of an example of an environment with an example ofan image moderation management computing apparatus;

FIG. 2 is a block diagram of the example of the image moderationmanagement computing apparatus shown in FIG. 1; and

FIG. 3 is a flow chart of an example of an improved method formoderating one or more images.

DETAILED DESCRIPTION

An exemplary network environment which incorporates an exemplary imagemoderation management system 10 is illustrated in FIGS. 1-2. In thisexample, the image moderation management system 10 includes an imagemoderation management computing apparatus 12 that is coupled to aplurality of moderator computing devices 14(1)-14(n), a plurality ofclient devices 16(1)-16(n), and a plurality of user devices 18(1)-18(n)via one or more communication network(s) 20, although the imagemoderation management computing apparatus 12, moderator computingdevices 14(1)-14(n), client devices 16(1)-16(n) and/or user devices18(1)-18(n) may be coupled together via other topologies. Additionally,the image moderation management system may include other system,devices, components, and/or elements which are well known in the art andthus will not be described herein. This technology provides a number ofadvantages including providing methods, non-transitory computer readablemedia, image moderation management systems, and image moderationmanagement computing apparatuses that provide more effective and easilycustomizable image moderation.

Referring to FIGS. 1-2, the image moderation management computingapparatus 12 of the image moderation management system may perform anynumber of functions including moderating one or more images asillustrated and described by way of the examples herein. The imagemoderation management computing apparatus 12 includes one or moreprocessors 22, a memory 24, and/or a communication interface 26, whichare coupled together by a bus or other communication link 28, althoughthe image moderation management computing apparatus 12 can include othertypes and/or numbers of elements in other configurations.

The processor(s) 22 of the image moderation management computingapparatus 12 may execute programmed instructions stored in the memory 24of the image moderation management computing apparatus 12 for the anynumber of the functions identified herein. The processor(s) 22 of theimage moderation management computing apparatus 12 may include one ormore CPUs or general purpose processors with one or more processingcores, for example, although other types of processor(s) can also beused.

The memory 24 of the image moderation management computing apparatus 12stores these programmed instructions for one or more aspects of theexamples of the technology as described and illustrated herein, althoughsome or all of the programmed instructions could be stored elsewhere. Avariety of different types of memory storage devices, such as randomaccess memory (RAM), read only memory (ROM), hard disk, solid statedrives, flash memory, or other computer readable medium which is readfrom and written to by a magnetic, optical, or other reading and writingsystem that is coupled to the processor(s), can be used for the memory.

Accordingly, the memory 24 of the image moderation management computingapparatus 12 can store one or more applications that can includecomputer executable instructions that, when executed by the imagemoderation management computing apparatus 12, cause the image moderationmanagement computing apparatus 12 to perform actions, such as tomoderate images, for example, and to perform other actions described andillustrated below with reference to FIGS. 1-3. The application(s) can beimplemented as modules or components of other applications. Further, theapplication(s) can be implemented as operating system extensions,module, plugins, or the like.

Even further, the application(s) may be operative in a cloud-basedcomputing environment. The application(s) can be executed within or asvirtual machine(s) or virtual server(s) that may be managed in acloud-based computing environment. Also, the application(s), and eventhe image moderation management computing apparatus 12 itself, may belocated in virtual server(s) running in a cloud-based computingenvironment rather than being tied to one or more specific physicalnetwork computing devices. Also, the application(s) may be running inone or more virtual machines (VMs) executing on the image moderationmanagement computing apparatus 12. Additionally, in one or moreembodiments of this technology, virtual machine(s) running on the imagemoderation management computing apparatus 12 may be managed orsupervised by a hypervisor.

In this particular example, the memory of the image moderationmanagement computing apparatus 12 includes one or more customizedcategories and corresponding threshold ranges database 30, one or morecomputer vision models 32, and/or a category rules database 34, althoughthe memory can include other policies, modules, databases, and/orapplications, for example. The one or more customized categories andcorresponding threshold ranges 30 may include different categories ofimages to moderate and may include and upper and lower threshold foreach to define a range of identified images which require further reviewand classification. The image moderation management computing apparatus12 may use deep learning techniques to analyze and compare a requestedimage with a computer vision model 32 to identify one of a plurality ofstored categories along with a percentage or probability of match.

By way of example, a deep learning technique or model is a stack oflayers which perform certain mathematical operations on input imagedata, such as convolution, data augmentation, or normalization by way ofexample only. These layers have a set of initial parameters calledweights which are tuned according to the category of data for the modelto converge. Additionally, the results of this analysis as well as anyfeedback from one of the moderator computing devices 14(1)-14(n) may beused to update the stored database of images for the categories and alsoto further enhance the one or more computer vision models 32 via deeplearning techniques for future analysis of requested images. One methodto train each of the computer vision data model(s) 32 is through asupervised deep learning model where a category of images, such as carsby way of example only, may be fed into the image moderation managementcomputing apparatus 12. Next, weights of the layers of supervised deeplearning model may be tuned by the image moderation management computingapparatus 12 for the model to converge on a given training data,computer data vision model 32 for the category of cars in this example,although other types and/or numbers of categories for other models maybe used Next, the image moderation management computing apparatus 12 maytest the computer vision model 32 against validation data to see if thenew model is more accurate and then process the computer vision model 32accordingly.

In another example of a method to train each of the computer vision datamodel(s) 32, the image moderation management computing apparatus 12 onlyuses images that are inaccurately classified by the current model, suchas images that are given an inaccurate or not high enough probability,to train the next iteration of the model, thus limiting the number ofimages used to train the model. Reducing the number of images used fortraining, decreases an amount of training time required while stillproviding an increasingly accurate model.

Further, the category rules database 34 may comprise one or morecustomizable rules, although the category rules database 34 may compriseother types and/or amounts of information. The one or more customizablerules can be executed on the image when a match above the upperthreshold, below the lower threshold and/or within the range and issubsequently confirmed to be or not to be a match for the particularcategory, such as to block the requested image or to request additionalverification before permitting the image to be viewed.

The communication interface 28 of the image moderation managementcomputing apparatus 12 operatively couples and communicates between theimage moderation management computing apparatus 12 and the moderatorcomputing devices 14(1)-14(n), the client devices 16(1)-16(n), and/orthe user devices 18(1)-18(n), which are all coupled together by the oneor more communication network(s) 20, although other types and/or numbersof communication networks or systems with other types and/or numbers ofconnections and/or configurations to other devices and/or elements canalso be used.

By way of example only, the one or more communication network(s) 20 mayinclude local area network(s) (LAN(s)) or wide area network(s) (WAN(s)),and can use TCP/IP over Ethernet and industry-standard protocols,although other types and/or numbers of protocols and/or communicationnetworks can be used. The communication network(s) 20 in this examplecan employ any suitable interface mechanisms and network communicationtechnologies including, for example, teletraffic in any suitable form(e.g., voice, modem, and the like), Public Switched Telephone Network(PSTNs), Ethernet-based Packet Data Networks (PDNs), combinationsthereof, and the like. The communication network(s) 20 can also includedirect connection(s) (e.g., for when a device illustrated in FIG. 1,such as the image moderation management computing apparatus 12, one ormore of the moderator computing devices 14(1)-14(n), one or more of theclient devices 16(1)-16(n), or one or more of the user devices18(1)-18(n), operate as virtual instances on the same physical machine.

While the image moderation management computing apparatus 12 isillustrated in this example as including a single device, the imagemoderation management computing apparatus 12 in other examples caninclude a plurality of devices or blades each having one or moreprocessors (each processor with one or more processing cores) thatimplement one or more steps of this technology. In these examples, oneor more of the devices can have a dedicated communication interface ormemory. Alternatively, one or more of the devices can utilize thememory, communication interface, or other hardware or softwarecomponents of one or more other devices included in the image moderationmanagement computing apparatus 12.

Additionally, one or more of the devices that together comprise theimage moderation management computing apparatus 12 in other examples canbe standalone devices or integrated with one or more other devices orapparatuses, such as one of the moderator computing devices 14(1)-14(n),for example. Moreover, one or more of the devices of the imagemoderation management computing apparatus 12 in these examples can be ina same or a different communication network including one or morepublic, private, or cloud networks, for example.

Each of the moderator computing devices 14(1)-14(n) of the imagemoderation management system 10 in this example includes one or moreprocessors, a memory, and a communication interface, which are coupledtogether by a bus or other communication link, although other numbersand/or types of network devices could be used. The moderator computingdevices 14(1)-14(n) in this example process receive and further classifysorted images falling within a dynamic range between lower and upperthresholds from the image moderation management computing apparatus 12via the one or more communication network(s) 20. The moderator computingdevices 14(1)-14(n) may be hardware or software or may represent asystem with multiple servers in a pool, which may include internal orexternal networks.

Although the moderator computing devices 14(1)-14(n) are illustrated assingle devices, one or more actions of each of the moderator computingdevices 14(1)-14(n) may be distributed across one or more distinctnetwork computing devices that together comprise one or more of themoderator computing devices 14(1)-14(n). Moreover, the moderatorcomputing devices 14(1)-14(n) are not limited to a particularconfiguration. The moderator computing devices 14(1)-14(n) may operateas a plurality of network computing devices within a clusterarchitecture, a peer-to peer architecture, virtual machines, or within acloud architecture, for example. Thus, the technology disclosed hereinis not to be construed as being limited to a single environment andother configurations and architectures are also envisaged.

The client devices 16(1)-16(n) of the image moderation management system10 in this example include any type of computing device that can requestimage moderation services, such as mobile computing devices, desktopcomputing devices, laptop computing devices, tablet computing devices,virtual machines (including cloud-based computers), or the like. Each ofthe client devices 16(1)-16(n) in this example includes a processor, amemory, and a communication interface, which are coupled together by abus or other communication link, although other numbers and/or types ofnetwork devices could be used.

The client devices 16(1)-16(n) may run interface applications, such asstandard Web browsers or standalone client applications, which mayprovide an interface to make requests for image moderation services andreceive response from the image moderation management computingapparatus 12 via the communication network(s) 20. The client devices16(1)-16(n) may further include a display device, such as a displayscreen or touchscreen, and/or an input device, such as a keyboard forexample.

The user devices 18(1)-18(n) of the image moderation management systemin this example include any type of computing device that can receive,render, and facilitate user interaction which may require moderation ofrequested or otherwise transmitted images, such as mobile computingdevices, desktop computing devices, laptop computing devices, tabletcomputing devices, virtual machines (including cloud-based computers),or the like. Each of the user devices 18(1)-18(n) in this exampleincludes a processor, a memory, and a communication interface, which arecoupled together by a bus or other communication link, although othernumbers and/or types of network devices could be used.

The user devices 18(1)-18(n) may run interface applications, such asstandard Web browsers or standalone client applications, which mayprovide an interface to make requests for, and receive content includingimages or may transmit images which may need to be moderated via thecommunication network(s) 20. The user devices 18(1)-18(n) may furtherinclude a display device, such as a display screen or touchscreen,and/or an input device, such as a keyboard for example.

Although the exemplary image moderation management system with the imagemoderation management computing apparatus 12, moderator computingdevices 14(1)-14(n), client devices 16(1)-16(n), user devices18(1)-18(n), and communication network(s) 20 are described andillustrated herein, other types and/or numbers of systems, devices,components, and/or elements in other topologies can be used. It is to beunderstood that the systems of the examples described herein are forexemplary purposes, as many variations of the specific hardware andsoftware used to implement the examples are possible, as will beappreciated by those skilled in the relevant art(s).

One or more of the components depicted in the image moderationmanagement system 10, such as the image moderation management computingapparatus 12, moderator computing devices 14(1)-14(n), client devices16(1)-16(n), or user devices 18(1)-18(n), for example, may be configuredto operate as virtual instances on the same physical machine. In otherwords, one or more of the image moderation management computingapparatus 12, moderator computing devices 14(1)-14(n), client devices16(1)-16(n), or user devices 18(1)-18(n) may operate on the samephysical device rather than as separate devices communicating throughcommunication network(s) 20. Additionally, there may be more or fewerimage moderation management computing apparatus 12, client devices16(1)-16(n), user devices 18(1)-18(n), or moderator computing devices14(1)-14(n) than illustrated in FIG. 1.

In addition, two or more computing systems or devices can be substitutedfor any one of the systems or devices in any example. Accordingly,principles and advantages of distributed processing, such as redundancyand replication also can be implemented, as desired, to increase therobustness and performance of the devices and systems of the examples.The examples may also be implemented on computer system(s) that extendacross any suitable network using any suitable interface mechanisms andtraffic technologies, including by way of example only teletraffic inany suitable form (e.g., voice and modem), wireless traffic networks,cellular traffic networks, Packet Data Networks (PDNs), the Internet,intranets, and combinations thereof.

The examples may also be embodied as one or more non-transitory computerreadable media having instructions stored thereon for one or moreaspects of the present technology as described and illustrated by way ofthe examples herein.

The instructions in some examples include executable code that, whenexecuted by one or more processors, cause the processors to carry outsteps necessary to implement the methods of the examples of thistechnology that are described and illustrated herein.

An exemplary improved method for moderating one or more images will nowbe described with reference to FIGS. 1-3. Referring more specifically toFIG. 3, in this example in step 100 a request that includes a UniformResource Locator (URL) of an image to be moderated is received by theimage moderation management computing apparatus 12, although othermanners for receiving or otherwise obtaining an image to be moderatedcould be used.

In step 102, the image moderation management computing apparatus 12executes one of the one or more computer vision models 32, which isbased on and updated by deep learning techniques as described herein, toidentify a probability or percentage match of the requested imageagainst stored images for the one or more different categories in thecustomized categories and corresponding threshold ranges database 30,although other types of image analysis to identify a percentage match orother correlation could be used. By way of example only, the imagemoderation management computing apparatus 12 may prepares each of theone or more computer vision models 30 by collecting and storing imagesfor the one or more different categories selected by the subscribing oneof the client devices 16(1)-16(n) for image moderation services. Next,the image moderation management computing apparatus 12 may feed thecollected images for one of the categories into one of the one or morecomputer vision models 30 which uses deep learning techniques toclassify or recognize the category or categories an image might fit in.Next, the one of the one or more computer vision models 30 in the imagemoderation management computing apparatus 12 may be used to determineand output the probability or percentage match that the received imageis in one of the one or more stored categories, although other mannersfor developing an computer vision model and/or other image analysistechniques may be used.

In step 104, the image moderation management computing apparatus 12determines when a determined probability or percentage match for thereceived image falls within a customizable range between upper and lowerthresholds for any of the one or more stored categories. In thisexample, for ease of illustration and description, falling within onlyone range for one category is generally described, although a receivedimage could fall within a customizable range of multiple categories.Since the approach used for managing each category which may be a matchis the same, the approach for managing multiple matches is not describedin detail in this example.

By way of example only, the subscribing one of the client devices16(1)-16(n) may have set for a particular image category a customizablelower threshold percentage match of 20% and above a customizable upperthreshold of 80%, i.e. a 60% range in this example of the received imagepossibly being in the category. If in this example, if the probabilityor percentage match for the image is within the range between the lowerthreshold percentage match of 20% and the upper threshold of 80% thenthe image moderation management computing apparatus 12 may send theimage to one of the moderator computing devices 14(1)-14(n) for furtheranalysis and classification. Additionally, in this example if theprobability or percentage match for the image is below the customizablelower threshold percentage match of 20% then the image moderationmanagement computing apparatus 12 may execute one or more of a pluralityof stored rules, set in this example by the subscribing one of theclient devices 16(1)-16(n), on the requested image, such as a rule toallow viewing of the requested image. Further, in this example if theprobability or percentage match is above the customizable upperthreshold percentage match of 80% then the image moderation managementcomputing apparatus 12 may execute one or more of the plurality ofstored rules, again set in this example by the subscribing one of theclient devices 16(1)-16(n), on the requested image, such as a rule toblock the requested image and/or to send a notice requesting furtherverification or approval to see the requested image.

The image moderation management computing apparatus 12 may already havestored in the customized categories and corresponding threshold rangesdatabase 30 one or more categories of images to moderate, such ascategories for nudity and violence by way of example only, and upper andlower thresholds for each of the categories. Again these may be set bythe subscribing one of the client devices 16(1)-16(n), although othermanners for setting these can be used. Accordingly, with this technologythe moderation of images can easily be customized based on not only aparticular setting or environment, such as at home or in an office, butalso based on one or more characteristics, such as age and/or culture,which could vary the ranges even in the same setting. By way of example,in a home setting the ranges could be set differently for a householdwith young children as opposed to adult children or could be adjustedbased differently in a home setting based on the particular culturalbackground.

If in step 104, the image moderation management computing apparatus 12determines that the probability or percentage falls outside of thecustomizable range, i.e. above the upper threshold or below the lowerthreshold, then the No branch is taken to step 106. In step 106, theimage moderation management computing apparatus 12 determines if theprobability or percentage match is above the upper threshold or belowthe lower threshold.

In step 108, the image moderation management computing apparatus 12determines which of one or more stored rules from the category rulesdatabase 34 to execute based on the determination of whether theprobability or percentage match is above the upper threshold or belowthe lower threshold. For example, if the probability or percentage isabove the upper threshold, then the image moderation managementcomputing apparatus 12 may execute one of the stored rules from thecategory rules database 34 to block the image or request additionalverification to grant access to the image, although other types and/ornumbers of rules may be executed. In another example, if the probabilityor percentage is below the lower threshold, then the image moderationmanagement computing apparatus 12 may execute other of the stored rulesfrom the category rules database 34 to allow the requested image. Oncecompleted, then this example of the method with respect to the receivedimage may end.

If back in step 104, the image moderation management computing apparatus12 determines that the probability or percentage falls within thecustomizable range, i.e. between the upper threshold and the lowerthreshold, then the Yes branch is taken to step 110. In step 110, theimage moderation management computing apparatus 12 sends the receivedimage or for example the URL for the image to one of the moderatorcomputing devices 14(1)-14(n). The one of the moderator computingdevices 14(1)-14(n) may then review the received image and theidentified one or more categories and then for each of the identifiedcategories generate and provide moderation analysis data regarding theprobability or percentage match as being either be above the upperthreshold or below the lower threshold for the one or more identifiedcategories. Additionally, the one of the moderator computing devices14(1)-14(n) may provide in the moderation analysis data additionalinformation which can be used to further refine the computer visionmodel 32.

In step 112, the image moderation management computing apparatus 12 mayobtain the moderation analysis data regarding the probability orpercentage match as being confirmed to either be above the upperthreshold or below the lower threshold for the one or more categoriesalong with information to be used to further refine the computer visionmodel 32. The image moderation management computing apparatus 12 alsomay forward the moderation analysis data to the subscribing one of theclient devices 16(1)-16(n).

In step 114, the image moderation management computing apparatus 12 maystore the received image and the moderation analysis data may be runthrough a training module in memory 24 using deep learning techniques tocreate an updated and more accurate computer vision model 32. By way ofexample, the training module executed by the image moderation managementcomputing apparatus 12 also can create live models in real-time so thecomputer vision model 32 is updated immediately or in real time. Withlive training, as soon as the image gets moderated and when the image iseither a false positive or a negative, the computer vision model 32 inthe image moderation management computing apparatus 12 is trained torectify for such mistakes and keep learning on the go. By way ofexample, the training module executed by the image moderation managementcomputing apparatus 12 also can create batches where the computer visionmodel 32 is periodically updated. With batch training, when asignificant number of misrecognized images have accumulated, then thecomputer vision model 32 in the image moderation management computingapparatus 12 is trained to rectify for such mistakes.

Following step 114, the image moderation management computing apparatus12 can proceed to steps 106 and 108 as described earlier based on themoderation analysis data for the received image which now would indicateeither a match, i.e. above the upper threshold, or no match, i.e. belowthe lower threshold.

Accordingly, as illustrated and described by way of the examples herein,the claimed technology provides more effective moderation of images,such as avatars, profile pictures, contest entries, and photo albumpictures by way of example. Additionally, with this technologycustomizable feedback from a moderator can be quickly and easilyincorporated in. Further with this technology, moderation of images caneasily be customized based not only on the particular setting orenvironment, but also based on one or more characteristics, such as ageand/or culture, by way of example.

Having thus described the basic concept of the invention, it will berather apparent to those skilled in the art that the foregoing detaileddisclosure is intended to be presented by way of example only, and isnot limiting. Various alterations, improvements, and modifications willoccur and are intended to those skilled in the art, though not expresslystated herein. These alterations, improvements, and modifications areintended to be suggested hereby, and are within the spirit and scope ofthe invention. Additionally, the recited order of processing elements orsequences, or the use of numbers, letters, or other designationstherefore, is not intended to limit the claimed processes to any orderexcept as may be specified in the claims. Accordingly, the invention islimited only by the following claims and equivalents thereto.

What is claimed is:
 1. A non-transitory computer readable medium havingstored thereon instructions for moderating one or more images comprisingexecutable code which when executed by one or more processors, causesthe one or more processors to: identify based on an automated imageanalysis a percentage match of an image against one or more computervision models for one or more categories to moderate with respect toaccessibility to view the image and which is based on a deep learningtechnique; determine when the identified percentage match is within arange between customizable lower and upper thresholds for the one ormore of the categories to moderate with respect to accessibility to viewthe image; obtain image moderation analysis data on adjusting theidentified percentage match of the image in at least one of thecategories to moderate with respect to accessibility to view the imagefrom one of one or more moderator computing devices when the identifiedpercentage match is determined to be within the range; adjust theidentified percentage match to an adjusted percentage match above theupper threshold or below the lower threshold based on the imagemoderation analysis data; and execute one of one or more stored rulesfrom a database on the image based on whether the identified percentagematch or the adjusted percentage match for the corresponding one of theone or more categories to moderate with respect to accessibility to viewthe image is above the upper threshold or below the lower threshold. 2.The non-transitory computer readable medium of claim 1, wherein theexecutable code when executed by the one or more processors furthercauses the one or more processors to update the one or more computervision models based on the obtained image moderation analysis data. 3.The non-transitory computer readable medium of claim 2, wherein theupdate is executed in a real time mode.
 4. The non-transitory computerreadable medium of claim 2, wherein the update is executed in a batchmode.
 5. The non-transitory computer readable medium of claim 1, whereinthe executable code when executed by the one or more processors furthercauses the one or more processors to execute a different one of one ormore stored rules on the image when the percentage match is above theupper threshold.
 6. The non-transitory computer readable medium of claim1, wherein the executable code when executed by the one or moreprocessors further causes the one or more processors to executing thestored programmed instructions to execute another different one of oneor more stored rules on the image when the percentage match is below thelower threshold.